Australian Frogs Show Distinct Seasonal Calling Patterns

Species within families vary widely in calling seasons, revealing ecological niche partitioning.

Analyzing FrogID citizen science data to reveal seasonal calling patterns across Australian frog species. Using ggbeeswarm plots to visualize how 135,154 recordings from 2023 show distinct seasonal preferences within taxonomic families, with Spring emerging as peak activity season.

Figure 1: Beeswarm plot showing seasonal patterns of frog calling in Australia. Each colored dot represents the recording frequency of one species, grouped by season (Summer to Spring) on the y-axis and log-scale recordings on the x-axis. White circles mark seasonal medians. Spring shows the highest activity with 81 species and 60,405 recordings, while Winter shows the lowest with 78 species and 32,002 recordings. Colors distinguish five major frog subfamilies (Hylid, Microhylidae, Myobatrachid, Toad, and Unknown), revealing diverse seasonal preferences within these taxonomic groups.

Steps to Create this Graphic

1. Load Packages & Setup

```{r}
#| label: load
#| warning: false
#| message: false
#| results: "hide"

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
if (!require("pacman")) install.packages("pacman")
pacman::p_load(
    tidyverse,   # Easily Install and Load the 'Tidyverse'
    ggtext,      # Improved Text Rendering Support for 'ggplot2'
    showtext,    # Using Fonts More Easily in R Graphs
    janitor,     # Simple Tools for Examining and Cleaning Dirty Data
    scales,      # Scale Functions for Visualization
    glue,        # Interpreted String Literals,
    ggbeeswarm,  # Categorical Scatter (Violin Point) Plots
    viridis,     # Colorblind-Friendly Color Maps for R
    lubridate    # Make Dealing with Dates a Little Easier
  )})

### |- figure size ----
camcorder::gg_record(
  dir    = here::here("temp_plots"),
  device = "png",
  width  = 8,
  height = 8,
  units  = "in",
  dpi    = 300
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))
```

2. Read in the Data

```{r}
#| label: read
#| include: true
#| eval: true
#| warning: false

tt <- tidytuesdayR::tt_load(2025, week = 35)

frogID_data <- tt$frogID_data |> clean_names()
frog_names <- tt$frog_names |> clean_names()

tidytuesdayR::readme(tt)
rm(tt)
```

3. Examine the Data

```{r}
#| label: examine
#| include: true
#| eval: true
#| results: 'hide'
#| warning: false

glimpse(frogID_data)
glimpse(frog_names)
```

4. Tidy Data

```{r}
#| label: tidy
#| warning: false

frog_data_clean <- frogID_data |>
  rename_all(~ str_replace_all(., "([A-Z])", "_\\1") |> str_to_lower()) |>
  rename_all(~ str_remove(., "^_")) |>
  mutate(
    month = month(event_date, label = TRUE),
    # Define seasons for Southern Hemisphere (Australia)
    season = case_when(
      month %in% c("Dec", "Jan", "Feb") ~ "Summer",
      month %in% c("Mar", "Apr", "May") ~ "Autumn",
      month %in% c("Jun", "Jul", "Aug") ~ "Winter",
      month %in% c("Sep", "Oct", "Nov") ~ "Spring"
    ),
    hour = hour(event_time)
  ) |>
  left_join(frog_names, by = "scientific_name") |>
  mutate(
    common_name = if_else(is.na(common_name) | common_name == "—",
      scientific_name, common_name
    ),
    state_province = str_trim(state_province),
    subfamily_clean = str_trim(coalesce(subfamily, "Unknown"))
  ) |>
  filter(!is.na(state_province), !is.na(season))

# Species seasonal activity
species_seasonal <- frog_data_clean |>
  filter(!is.na(subfamily_clean)) |>
  # Get species with enough records
  group_by(scientific_name) |>
  filter(n() >= 50) |>
  ungroup() |>
  # Count by species and season
  count(scientific_name, common_name, subfamily_clean, season) |>
  # Get top subfamilies
  group_by(subfamily_clean) |>
  mutate(total_subfamily_records = sum(n)) |>
  ungroup() |>
  arrange(desc(total_subfamily_records)) |>
  filter(subfamily_clean %in% unique(subfamily_clean)[1:5]) |> # Top 5 for clarity
  mutate(
    season_ordered = factor(season, levels = c("Spring", "Winter", "Autumn", "Summer"))
  )

# Calculate medians for reference
season_medians <- species_seasonal |>
  group_by(season_ordered) |>
  summarise(median_recordings = median(n), .groups = "drop")

# Add context metrics
season_stats <- species_seasonal |>
  group_by(season_ordered) |>
  summarise(
    total_species = n(),
    total_recordings = sum(n),
    median_recordings = median(n),
    .groups = "drop"
  ) |>
  mutate(
    season_label = paste0(
      season_ordered, "\n(", total_species, " species,\n",
      scales::comma(total_recordings), " recordings)"
    )
  )
```

5. Visualization Parameters

```{r}
#| label: params
#| include: true
#| warning: false

### |-  plot aesthetics ----
# Get basic theme colors
colors <- get_theme_colors()

### |- titles and caption ----
title_text <- str_glue("Australian Frogs Show Distinct Seasonal Calling Patterns")

subtitle_text <- str_glue(
    "Species within families vary widely in calling seasons, revealing ecological niche partitioning<br>",
    "**Spring** emerges as peak season with highest species diversity and recording activity<br><br>",
    "FrogID citizen science data: **Jan 1 - Nov 9, 2023** (n=135,154 total recordings)<br>",
    "**White circles** show seasonal medians | **Each dot** represents one frog species"
)

caption_text <- create_social_caption(
  tt_year = 2025,
  tt_week = 35,
  source_text = "FrogID dataset 6.0"
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----
# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    # Text styling
    plot.title = element_text(face = "bold", family = fonts$title, size = rel(1.2), color = colors$title, margin = margin(b = 10)),
    plot.subtitle = element_text(family = fonts$subtitle, lineheight = 1.2, color = colors$subtitle, size = rel(0.78), margin = margin(b = 20)),

    # Axis elements
    axis.line = element_blank(),
    axis.ticks = element_blank(),

    # Grid elements
    panel.grid.major = element_line(color = "gray90", linetype = "solid", linewidth = 0.3),
    # panel.grid.major.y = element_blank(),
    panel.grid.minor.x = element_blank(),
    panel.grid.minor.y = element_blank(),

    # Axis elements
    axis.text = element_text(color = colors$text, size = rel(0.7)),
    axis.title.x = element_text(color = colors$text, face = "bold", size = rel(0.8), margin = margin(t = 15)),
    axis.title.y = element_text(color = colors$text, face = "bold", size = rel(0.8), margin = margin(r = 10)),

    # Legend elements
    legend.position = "plot",
    legend.title = element_text(family = fonts$tsubtitle, color = colors$text, size = rel(0.8), face = "bold"),
    legend.text = element_text(family = fonts$tsubtitle, color = colors$text, size = rel(0.7)),
    legend.margin = margin(t = 15),

    # Plot margin
    plot.margin = margin(t = 15, r = 15, b = 15, l = 15),
  )
)

# Set theme
theme_set(weekly_theme)
```

6. Plot

```{r}
#| label: plot
#| warning: false

### |-  final plot ----
p <- species_seasonal |>
  ggplot(aes(y = season_ordered, x = n)) +
  # Geoms
  geom_beeswarm(aes(color = subfamily_clean),
    size = 2.5, alpha = 0.7, cex = 1
  ) +
  geom_point(
    data = season_stats,
    aes(y = season_ordered, x = median_recordings),
    shape = 21, size = 4, fill = "white", color = "black", stroke = 1.2
  ) +
  # Scales
  scale_color_viridis_d(name = "Frog Subfamily", option = "plasma") +
  scale_x_log10(labels = comma_format()) +
  scale_y_discrete(labels = season_stats$season_label) +
  # Labs
  labs(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    y = NULL,
    x = "Recordings per Species (Log Scale)"
  ) +
  # Theme
  theme(
    axis.text.y = element_text(size = 10, hjust = 0.5),
    legend.position = "right",
    plot.title = element_text(
      size = rel(1.5),
      family = fonts$title,
      face = "bold",
      color = colors$title,
      lineheight = 1.1,
      margin = margin(t = 5, b = 5)
    ),
    plot.subtitle = element_markdown(
      size = rel(0.80),
      family = fonts$subtitle,
      color = alpha(colors$subtitle, 0.9),
      lineheight = 1.2,
      margin = margin(t = 5, b = 10)
    ),
    plot.caption = element_markdown(
      size = rel(0.5),
      family = fonts$caption,
      color = colors$caption,
      hjust = 0.5,
      margin = margin(t = 10)
    )
  )
```

7. Save

```{r}
#| label: save
#| warning: false

### |-  plot image ----  
save_plot(
  plot = p, 
  type = "tidytuesday", 
  year = 2025, 
  week = 35, 
  width  = 8,
  height = 8
  )
```

8. Session Info

Expand for Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] here_1.0.1        viridis_0.6.5     viridisLite_0.4.2 ggbeeswarm_0.7.2 
 [5] glue_1.8.0        scales_1.3.0      janitor_2.2.0     showtext_0.9-7   
 [9] showtextdb_3.0    sysfonts_0.8.9    ggtext_0.1.2      lubridate_1.9.3  
[13] forcats_1.0.0     stringr_1.5.1     dplyr_1.1.4       purrr_1.0.2      
[17] readr_2.1.5       tidyr_1.3.1       tibble_3.2.1      ggplot2_3.5.1    
[21] tidyverse_2.0.0   pacman_0.5.1     

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.1   vipor_0.4.7        farver_2.1.2       fastmap_1.2.0     
 [5] gh_1.4.1           digest_0.6.37      timechange_0.3.0   lifecycle_1.0.4   
 [9] rsvg_2.6.1         magrittr_2.0.3     compiler_4.4.0     rlang_1.1.6       
[13] tools_4.4.0        utf8_1.2.4         yaml_2.3.10        knitr_1.49        
[17] htmlwidgets_1.6.4  bit_4.5.0          curl_6.0.0         xml2_1.3.6        
[21] camcorder_0.1.0    tidytuesdayR_1.1.2 withr_3.0.2        grid_4.4.0        
[25] fansi_1.0.6        colorspace_2.1-1   gitcreds_0.1.2     cli_3.6.4         
[29] rmarkdown_2.29     crayon_1.5.3       ragg_1.3.3         generics_0.1.3    
[33] rstudioapi_0.17.1  tzdb_0.5.0         commonmark_1.9.2   parallel_4.4.0    
[37] vctrs_0.6.5        jsonlite_1.8.9     hms_1.1.3          bit64_4.5.2       
[41] beeswarm_0.4.0     systemfonts_1.1.0  magick_2.8.5       gifski_1.32.0-1   
[45] codetools_0.2-20   stringi_1.8.4      gtable_0.3.6       munsell_0.5.1     
[49] pillar_1.9.0       rappdirs_0.3.3     htmltools_0.5.8.1  R6_2.5.1          
[53] httr2_1.0.6        textshaping_0.4.0  rprojroot_2.0.4    vroom_1.6.5       
[57] evaluate_1.0.1     markdown_1.13      gridtext_0.1.5     snakecase_0.11.1  
[61] renv_1.0.3         Rcpp_1.0.13-1      svglite_2.1.3      gridExtra_2.3     
[65] xfun_0.49          pkgconfig_2.0.3   

9. GitHub Repository

Expand for GitHub Repo

The complete code for this analysis is available in tt_2025_35.qmd.

For the full repository, click here.

10. References

Expand for References

1. Data Sources:

• TidyTuesday 2025 Week 35: [Australian Frogs](https://github.com/rfordatascience/tidytuesday/blob/main/data/2025/2025-09-02)